CEF: Funded projects


Funded projects

  • Improving inverse abstraction based neural network verification using automated machine learning techniques
    Matthias könig PhD at Leiden University Abstract: This project seeks to advance the state of the art in formal neural network verification. Formal neural network verification methods check whether a trained neural network, for example an image classifier, satisfies certain properties or guarantees regarding its behaviour, such as correctness, robustness, or safety, under various inputs … Read more
  • Towards Stable and Robust Learning with Limited Labelled Data: Investigating the Impact of Data Choice
    Branislav Pecher PhD at Kempelen Institute of Intelligent Technologies, member of Slovak.AI Abstract: Learning with limited labelled data, such as meta-learning, transfer learning or in-context learning, aims to effectively train a model using only a small amount of labelled samples. However, there is still limited understanding of the required settings or characteristics for these approaches … Read more
  • Graph Representation Learning for Solving Combinatorial Optimization Problems
    Ya Song PhD student at Eindhoven University of Technology Abstract: In the research field of solving combinatorial optimization problems, many studies have considered combining machine learning with optimization algorithms and proposed so-called learning-based optimization algorithms. Compared to traditional handcrafted algorithms, these methods can automatically extract relevant knowledge from training data and require less domain knowledge. In … Read more
  • Causal Analysis for Fairness of AI Models
    Martina cinquini PhD student at the University of Pisa Abstract: Artificial Intelligence (AI) has become ubiquitous in many sensitive domains where individuals and society can potentially be harmed by its outputs. In an attempt to reduce the ethical or legal implications of AI-based decisions, the scientific community’s interest in fairness-aware Machine Learning has been increasingly … Read more
  • How we trust robots: Attribution of Intentionality, Anthropomorphism and Uncanny Valley Effect
    Martina Bacaro PhD student at the University of Bologna – Alma Mater Studiorum Abstract: Interactions between humans and robots are increasing both in specialistic and everyday scenarios. Trustworthiness is acknowledged as a key factor for successful engagements between humans and robots. For humans to understand and rely on robots’ actions and intentions, they need to … Read more